Traditional feature-level adaptations [1]}, [2]}, [3]} consider two aspects in dealing with the problem discussed above. First, these methods train a model \(G\)  to distill knowledge from labeled data by minimizing the task loss in the source domain, which is formalized as a supervised problem:
